Stimuli-SoS: a model-based approach to derive stimuli generators for simulations of systems-of-systems software architectures

Abstract

Background: Systems-of-systems (SoS) are alliances of independent and interoperable software-intensive systems. SoS often support critical domains, being required to exhibit a reliable operation, specially because people's safety relies on their services. In this direction, simulations enable the validation of different operational scenarios in a controlled environment, allowing a benchmarking of its response as well as revealing possible breaches that could lead to failures. However, simulations are traditionally manual, demanding a high level of human intervention, being costly and error-prone. A stimuli generator could aid in by continuously providing data to trigger a SoS simulation and maintaining its operation. Methods: We established a model-based approach termed Stimuli-SoS to support the creation of stimuli generators to be used in SoS simulations. Stimuli-SoS uses software architecture descriptions for automating the creation of such generators. Specifically, this approach transforms SoSADL, a formal architectural description language for SoS, into dynamic models expressed in DEVS, a simulation formalism. We carried out a case study in which Stimuli-SoS was used to automatically produce stimuli generators for a simulation of a flood monitoring SoS. Results:We run simulations of a SoS architectural configuration with 69 constituent systems, i.e., 42 sensors, 9 crowdsourcing systems, and 18 drones. Stimuli generators were automatically generated for each type of constituent. These stimuli generators were capable of receiving the input data from the database and generating the expected stimuli for the constituents, allowing to simulate constituent systems interoperations into the flood monitoring SoS. Using Stimuli-SoS, we simulated 38 days of flood monitoring in little more than 6 h. Stimuli generators correctly forwarded data to the simulation, which was able to reproduce 29 flood alerts triggered by the SoS during a flooding event. In particular, Stimuli-SoS is almost 65 times more productive than a manual approach to producing data for the same type of simulation. Conclusions: Our approach succeeded in automatically deriving a functional stimuli generator that can reproduce environmental conditions for simulating a SoS. In particular, we presented new contributions regarding productivity and automation for the use of a model-based approach in SoS engineering.

Tópicos em Sistemas de Informação: Minicursos SBSI 2017

“…Neste sentido, é essencial garantir que SoS sejam aplicações baseadas em software com operação confiável e robusta. Em virtude disso, os modelos de desenvolvimento de SoS têm sido intensamente voltados às atividades de verificação e validação (V&V) (formal inclusive, se possível), uma vez que V&V pode reduzir o número de falhas esperadas, proporcionando um nível de confiança suficientemente elevado para ser considerado aceitável para estar disponível no mercado [Graciano Neto et al 2017b]. Sabe-se que toda atividade de verificação e validação exige algum nível de especificação que possibilite a execução destas atividades.…”

“…Eles são o elemento central de vários empreendimentos bem-sucedidos e apoiam atividades sociais e industriais essenciais. SI estão presentes na forma de Sistemas de Apoio à Decisão, Sistemas de Informação Empresariais, Redes Sociais e Sistemas de Informação Meteorológica, oferecendo serviços valiosos para beneficiar a sociedade ] [Graciano Neto et al 2017a2017b]. À medida que as tecnologias evoluem, o SI também evolui, sendo pressionado a realizar tarefas mais complexas, estabelecendo novas relações para oferecer funcionalidades não triviais.…”

Simulation-based Optimization of System-of-Systems Software Architectures

“…Emergent behavior. As constituents interoperate, the SoS can present some capabilities composed by individual smaller functionalities performed by the constituents, achieving emergent behaviors, and the main purposes of the SoS OQUENDO;NAKAGAWA, 2017;OQUENDO, 2017). SoS behaviors can be classified as: Simple, in which emergent behaviors are emergent properties readily predicted by simplified models of the SoS; Predicted, in which emergent behaviors are those readily and consistently reproducible in simulations of the system, but not in static models; Strong, emergent behaviors are consistent with SoS known properties, but are not reproducible in any model of the system and simulations may reproduce the behavior, but inconsistently; and spooky, in which emergent behaviors are inconsistent with known properties of the SoS, not reproducible and not subject to simulation;…”

“…The languages currently adopted to describe software architectures, such as Unified Modeling Language (UML 1 ), Systems Modeling Language (SysML 2 ), and Compass Modeling Language (CML 3 ), lack sufficient accuracy to effectively describe SoS architectures, specially in regards to (GUESSI et al, 2015;ZEIGLER et al, 2012;VLIET, 2013;CAVALCANTE et al, 2016;NETO et al, 2014;XIA et al, 2013;OQUENDO;BATISTA, 2014): (i) dynamic architecture; (ii) the description of constituents unknown at design time; (iii) environment modeling; (iv) the combination of formal foundations and high-level abstraction notations; and (v) the concept of SoS software architecture with adequate detail to guarantee precision in representation. UML, for instance, does not support a suitable specification of SoS software architectures, since a notation for specifying dynamic architectures is lacking (GUESSI et al, 2015).…”

“…DEVS is a well-known simulation language (ZEIGLER et al, 2012;NETO et al, 2017). It consists of a modeling formalism based on the idea of atomic and coupled models.…”